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The human genome contains millions of candidatecis-regulatory elements (CREs) with cell-type-specific activities that shape both health and myriad disease states. However, we lack a functional understanding of the sequence features that control the activity and cell-type-specific features of these CREs. Here, we used lentivirus-based massively parallel reporter assays (lentiMPRAs) to test the regulatory activity of over 680,000 sequences, representing a nearly comprehensive set of all annotated CREs among three cell types (HepG2, K562, and WTC11), finding 41.7% to be functional. By testing sequences in both orientations, we find promoters to have significant strand orientation effects. We also observe that their 200 nucleotide cores function as non-cell-type-specific ‘on switches’ providing similar expression levels to their associated gene. In contrast, enhancers have weaker orientation effects, but increased tissue-specific characteristics. Utilizing our lentiMPRA data, we develop sequence-based models to predict CRE function with high accuracy and delineate regulatory motifs. Testing an additional lentiMPRA library encompassing 60,000 CREs in all three cell types, we further identified factors that determine cell-type specificity. Collectively, our work provides an exhaustive catalog of functional CREs in three widely used cell lines, and showcases how large-scale functional measurements can be used to dissect regulatory grammar.

Context Many different inherited and acquired conditions can result in premature bone fragility/low bone mass disorders (LBMDs). Objective We aimed to elucidate the impact of genetic testing on differential diagnosis of adult LBMDs and at defining clinical criteria for predicting monogenic forms. Methods Four clinical centers broadly recruited a cohort of 394 unrelated adult women before menopause and men younger than 55 years with a bone mineral density (BMD) Z-score Results In total, 20.8% of the participants carried rare disease-causing variants (DCVs) in genes known to cause osteogenesis imperfecta (COL1A1, COL1A2), hypophosphatasia (ALPL), and early-onset osteoporosis (LRP5, PLS3, and WNT1). In addition, we identified rare DCVs in ENPP1, LMNA, NOTCH2, and ZNF469. Three individuals had autosomal recessive, 75 autosomal dominant, and 4 X-linked disorders. A total of 9.7% of the participants harbored variants of unknown significance. A regression analysis revealed that the likelihood of detecting a DCV correlated with a positive family history of osteoporosis, peripheral fractures (> 2), and a high normal body mass index (BMI). In contrast, mutation frequencies did not correlate with age, prevalent vertebral fractures, BMD, or biochemical parameters. In individuals without monogenic disease-causing rare variants, common variants predisposing for low BMD (eg, in LRP5) were overrepresented. Conclusion The overlapping spectra of monogenic adult LBMD can be easily disentangled by genetic testing and the proposed clinical criteria can help to maximize the diagnostic yield.

Background Genome sequencing efforts for individuals with rare Mendelian disease have increased the research focus on the noncoding genome and the clinical need for methods that prioritize potentially disease causal noncoding variants. Some tools for assessment of variant pathogenicity as well as annotations are not available for the current human genome build (GRCh38), for which the adoption in databases, software, and pipelines was slow. Results Here, we present an updated version of the Regulatory Mendelian Mutation (ReMM) score, retrained on features and variants derived from the GRCh38 genome build. Like its GRCh37 version, it achieves good performance on its highly imbalanced data. To improve accessibility and provide users with a toolbox to score their variant files and look up scores in the genome, we developed a website and API for easy score lookup. Conclusions Scores of the GRCh38 genome build are highly correlated to the prior release with a performance increase due to the better coverage of features. For prioritization of noncoding mutations in imbalanced datasets, the ReMM score performed much better than other variation scores. Prescored whole-genome files of GRCh37 and GRCh38 genome builds are cited in the article and the website; UCSC genome browser tracks, and an API are available at https://remm.bihealth.org.

Notch signaling is an established developmental pathway for brain morphogenesis. Given that Delta-like 1 (DLL1) is a ligand for the Notch receptor and that a few individuals with developmental delay, intellectual disability, and brain malformations have microdeletions encompassing DLL1, we hypothesized that insufficiency of DLL1 causes a human neurodevelopmental disorder. We performed exome sequencing in individuals with neurodevelopmental disorders. The cohort was identified using known Matchmaker Exchange nodes such as GeneMatcher. This method identified 15 individuals from 12 unrelated families with heterozygous pathogenic DLL1 variants (nonsense, missense, splice site, and one whole gene deletion). The most common features in our cohort were intellectual disability, autism spectrum disorder, seizures, variable brain malformations, muscular hypotonia, and scoliosis. We did not identify an obvious genotype-phenotype correlation. Analysis of one splice site variant showed an in-frame insertion of 12 bp. In conclusion, heterozygous DLL1 pathogenic variants cause a variable neurodevelopmental phenotype and multi-systemic features. The clinical and molecular data support haploinsufficiency as a mechanism for the pathogenesis of this DLL1-related disorder and affirm the importance of DLL1 in human brain development.

Massively parallel reporter assays (MPRAs) can simultaneously measure the function of thousands of candidate regulatory sequences (CRSs) in a quantitative manner. In this method, CRSs are cloned upstream of a minimal promoter and reporter gene, alongside a unique barcode, and introduced into cells. If the CRS is a functional regulatory element, it will lead to the transcription of the barcode sequence, which is measured via RNA sequencing and normalized for cellular integration via DNA sequencing of the barcode. This technology has been used to test thousands of sequences and their variants for regulatory activity, to decipher the regulatory code and its evolution, and to develop genetic switches. Lentivirus-based MPRA (lentiMPRA) produces ‘in-genome’ readouts and enables the use of this technique in hard-to-transfect cells. Here, we provide a detailed protocol for lentiMPRA, along with a user-friendly Nextflow-based computational pipeline—MPRAflow—for quantifying CRS activity from different MPRA designs. The lentiMPRA protocol takes ~2 months, which includes sequencing turnaround time and data processing with MPRAflow.

Several prediction problems in Computational Biology and Genomic Medicine are characterized by both big data as well as a high imbalance between examples to be learned, whereby positive examples can represent a tiny minority with respect to negative examples. For instance, deleterious or pathogenic variants are overwhelmed by the sea of neutral variants in the non-coding regions of the genome: as a consequence the prediction of deleterious variants is a very challenging highly imbalanced classification problem, and classical prediction tools fail to detect the rare pathogenic examples among the huge amount of neutral variants or undergo severe restrictions in managing big genomic data. To overcome these limitations we propose parSMURF, a method that adopts a hyper-ensemble approach and oversampling and undersampling techniques to deal with imbalanced data, and parallel computational techniques to both manage big genomic data and significantly speed-up the computation. The synergy between Bayesian optimization techniques and the parallel nature of parSMURF enables efficient and user-friendly automatic tuning of the hyper-parameters of the algorithm, and allows specific learning problems in Genomic Medicine to be easily fit. Moreover, by using MPI parallel and machine learning ensemble techniques, parSMURF can manage big data by partitioning them across the nodes of a High Performance Computing cluster.Results with synthetic data and with single nucleotide variants associated with Mendelian diseases and with GWAS hits in the non-coding regions of the human genome, involving millions of examples, show that parSMURF achieves state-of-the-art results and a speed-up of 80× with respect to the sequential version.In conclusion parSMURF is a parallel machine learning tool that can be trained to learn different genomic problems, and its multiple levels of parallelization and its high scalability allow us to efficiently fit problems characterized by big and imbalanced genomic data.Availability and ImplementationThe C++ OpenMP multi-core version tailored to a single workstation and the C++ MPI/OpenMP hybrid multi-core and multi-node parSMURF version tailored to a High Performance Computing cluster are both available from github: https://github.com/AnacletoLAB/parSMURF

The annotation and characterization of tissue-specific cis-regulatory elements (CREs) in non-coding DNA represents an open challenge in computational genomics. Several prior works show that machine learning methods, using epigenetic or spectral features directly extracted from DNA sequences, can predict active promoters and enhancers in specific tissues or cell lines. In particular, very recently deep-learning techniques obtained state-of-the-art results in this challenging computational task. In this study, we provide additional evidence that Feed Forward Neural Networks (FFNN) trained on epigenetic data and one-dimensional convolutional neural networks (CNN) trained on DNA sequence data can successfully predict active regulatory regions in different cell lines. We show that model selection by means of Bayesian optimization applied to both FFNN and CNN models can significantly improve deep neural network performance, by automatically finding models that best fit the data. Further, we show that techniques applied to balance active and non-active regulatory regions in the human genome in training and test data may lead to over-optimistic or poor predictions. We recommend to use actual imbalanced data that was not used to train the models for evaluating their generalization performance.

A genome-wide evaluation of the effects of ionizing radiation on mutation induction in the mouse germline has identified multisite de novo mutations (MSDNs) as marker for previous exposure. Here we present the results of a small pilot study of whole genome sequencing in offspring of soldiers who served in radar units on weapon systems that were emitting high-frequency radiation. We found cases of exceptionally high MSDN rates as well as an increased mean in our cohort: While a MSDN mutation is detected in average in 1 out of 5 offspring of unexposed controls, we observed 12 MSDNs in altogether 18 offspring, including a family with 6 MSDNs in 3 offspring. Moreover, we found two translocations, also resulting from neighboring mutations. Our findings indicate that MSDNs might be suited in principle for the assessment of DNA damage from ionizing radiation also in humans. However, as exact person-related dose values in risk groups are usually not available, the interpretation of MSDNs in single families would benefit from larger molecular epidemiologic studies on this new biomarker.

The integrative analysis of high-throughput reporter assays, machine learning, and profiles of epigenomic chromatin state in a broad array of cells and tissues has the potential to significantly improve our understanding of noncoding regulatory element function and its contribution to human disease. Here, we report results from the CAGI 5 regulation saturation challenge where participants were asked to predict the impact of nucleotide substitution at every base pair within five disease-associated human enhancers and nine disease-associated promoters. A library of mutations covering all bases was generated by saturation mutagenesis and altered activity was assessed in a massively parallel reporter assay (MPRA) in relevant cell lines. Reporter expression was measured relative to plasmid DNA to determine the impact of variants. The challenge was to predict the functional effects of variants on reporter expression. Comparative analysis of the full range of submitted prediction results identifies the most successful models of transcription factor binding sites, machine learning algorithms, and ways to choose among or incorporate diverse datatypes and cell-types for training computational models. These results have the potential to improve the design of future studies on more diverse sets of regulatory elements and aid the interpretation of disease-associated genetic variation.

The majority of common variants associated with common diseases, as well as an unknown proportion of causal mutations for rare diseases, fall in noncoding regions of the genome. Although catalogs of noncoding regulatory elements are steadily improving, we have a limited understanding of the functional effects of mutations within them. Here, we perform saturation mutagenesis in conjunction with massively parallel reporter assays on 20 disease-associated gene promoters and enhancers, generating functional measurements for over 30,000 single nucleotide substitutions and deletions. We find that the density of putative transcription factor binding sites varies widely between regulatory elements, as does the extent to which evolutionary conservation or integrative scores predict functional effects. These data provide a powerful resource for interpreting the pathogenicity of clinically observed mutations in these disease-associated regulatory elements, and comprise a rich dataset for the further development of algorithms that aim to predict the regulatory effects of noncoding mutations., Interpreting genetic variation in the noncoding genome remains challenging, with functional effects difficult to predict. Here, the authors perform saturation mutagenesis combined with massively parallel reporter assays for 20 disease-associated regulatory elements, quantifying the effects of over 30,000 variants.

The Human Phenotype Ontology (HPO) provides a standard categorization of the phenotypic abnormalities encountered in human diseases and of the semantic relationship between them. Quite surprisingly the problem of the automated prediction of the association between genes and abnormal human phenotypes has been widely overlooked, even if this issue represents an important step toward the characterization of gene-disease associations, especially when no or very limited knowledge is available about the genetic etiology of the disease under study. We present a novel ensemble method able to capture the hierarchical relationships between HPO terms, and able to improve existing hierarchical ensemble algorithms by explicitly considering the predictions of the descendant terms of the ontology. In this way the algorithm exploits the information embedded in the most specific ontology terms that closely characterize the phenotypic information associated with each human gene. Genome-wide results obtained by integrating multiple sources of information show the effectiveness of the proposed approach.

The majority of common variants associated with common diseases, as well as an unknown proportion of causal mutations for rare diseases, fall in noncoding regions of the genome. Although catalogs of noncoding regulatory elements are steadily improving, we have a limited understanding of the functional effects of mutations within them. Here, we performed saturation mutagenesis in conjunction with massively parallel reporter assays on 20 disease-associated gene promoters and enhancers, generating functional measurements for over 30,000 single nucleotide substitution and deletion mutations. We find that the density of putative transcription factor binding sites varies widely between regulatory elements, as does the extent to which evolutionary conservation or various integrative scores predict functional effects. These data provide a powerful resource for interpreting the pathogenicity of clinically observed mutations in these disease-associated regulatory elements, and also comprise a gold-standard dataset for the further development of algorithms that aim to predict the regulatory effects of noncoding mutations.

PURPOSE: Phenotype information is crucial for the interpretation of genomic variants. So far it has only been accessible for bioinformatics workflows after encoding into clinical terms by expert dysmorphologists. METHODS: Here, we introduce an approach driven by artificial intelligence that uses portrait photographs for the interpretation of clinical exome data. We measured the value added by computer-assisted image analysis to the diagnostic yield on a cohort consisting of 679 individuals with 105 different monogenic disorders. For each case in the cohort we compiled frontal photos, clinical features, and the disease-causing variants, and simulated multiple exomes of different ethnic backgrounds. RESULTS: The additional use of similarity scores from computer-assisted analysis of frontal photos improved the top 1 accuracy rate by more than 20-89% and the top 10 accuracy rate by more than 5-99% for the disease-causing gene. CONCLUSION: Image analysis by deep-learning algorithms can be used to quantify the phenotypic similarity (PP4 criterion of the American College of Medical Genetics and Genomics guidelines) and to advance the performance of bioinformatics pipelines for exome analysis. ispartof: GENETICS IN MEDICINE vol:21 issue:12 pages:2807-2814 ispartof: location:United States status: published

BackgroundGlycosylphosphatidylinositol Biosynthesis Defects (GPIBDs) cause a group of phenotypically overlapping recessive syndromes with intellectual disability, for which pathogenic mutations have been described in 16 genes of the corresponding molecular pathway. An elevated serum activity of alkaline phosphatase (AP), a GPI-linked enzyme, has been used to assign GPIBDs to the phenotypic series of Hyperphosphatasia with Mental Retardation Syndrome (HPMRS) and to distinguish them from another subset of GPIBDs, termed Multiple Congenital Anomalies Hypotonia Seizures syndrome (MCAHS). However, the increasing number of individuals with a GPIBD shows that hyperphosphatasia is a variable feature that is not ideal for a clinical classification.MethodsWe studied the discriminatory power of multiple GPI-linked substrates that were assessed by flow cytometry in blood cells and fibroblasts of 39 and 14 individuals with a GPIBD, respectively. On the phenotypic level, we evaluated the frequency of occurrence of clinical symptoms and analyzed the performance of computer-assisted image analysis of the facial gestalt in 91 individuals.ResultsWe found that certain malformations such as Morbus Hirschsprung and Diaphragmatic defects are more likely to be associated with particular gene defects (PIGV, PGAP3, PIGN). However, especially at the severe end of the clinical spectrum of HPMRS, there is a high phenotypic overlap with MCAHS. Elevation of AP has also been documented in some of the individuals with MCAHS, namely those with PIGA mutations. Although the impairment of GPI-linked substrates is supposed to play the key role in the pathophysiology of GPIBDs, we could not observe gene-specific profiles for flow cytometric markers or a correlation between their cell surface levels and the severity of the phenotype. In contrast, it was facial recognition software that achieved the highest accuracy in predicting the disease-causing gene in a GPIBD.ConclusionsDue to the overlapping clinical spectrum of both, HPMRS and MCAHS, in the majority of affected individuals, the elevation of AP and the reduced surface levels of GPI-linked markers in both groups, a common classification as GPIBDs is recommended. The effectiveness of computer-assisted gestalt analysis for the correct gene inference in a GPIBD and probably beyond is remarkable and illustrates how the information contained in human faces is pivotal in the delineation of genetic entities.

Exomiser is an application that prioritizes genes and variants in next-generation sequencing (NGS) projects for novel disease-gene discovery or differential diagnostics of Mendelian disease. Exomiser comprises a suite of algorithms for prioritizing exome sequences using random-walk analysis of protein interaction networks, clinical relevance and cross-species phenotype comparisons, as well as a wide range of other computational filters for variant frequency, predicted pathogenicity and pedigree analysis. In this protocol, we provide a detailed explanation of how to install Exomiser and use it to prioritize exome sequences in a number of scenarios. Exomiser requires ~3 GB of RAM and roughly 15–90 s of computing time on a standard desktop computer to analyze a variant call format (VCF) file. Exomiser is freely available for academic use from http://www.sanger.ac.uk/science/tools/exomiser.

The apoptosis-inducing factor (AIF) functions as a FAD-dependent NADH oxidase in mitochondria. Upon apoptotic stimulation it is released from mitochondria and migrates to the nucleus where it induces chromatin condensation and DNA fragmentation. So far mutations in AIFM1, a X-chromosomal gene coding for AIF, have been described in three families with 11 affected males. We report here on a further patient thereby expanding the clinical and mutation spectrum. In addition, we review the known phenotypes related to AIFM1 mutations. The clinical course in the male patient described here was characterized by phases with rapid deterioration and long phases without obvious progression of disease. At age 2.5 years he developed hearing loss and severe ataxia and at age 10 years muscle wasting, swallowing difficulties, respiratory insufficiency and external opthamoplegia. By next generation sequencing of whole exome we identified a hemizygous missense mutation in the AIFM1 gene, c.727G>T (p.Val243Leu) affecting a highly conserved residue in the FAD-binding domain. Summarizing what is known today, mutations in AIFM1 are associated with a progressive disorder with myopathy, ataxia and neuropathy. Severity varies greatly even within one family with onset of symptoms between birth and adolescence. 3 of 12 patients died before age 5 years while others were still able to walk during young adulthood. Less frequent symptoms were hearing loss, seizures and psychomotor regression. Results from clinical chemistry, brain imaging and muscle biopsy were unspecific and inconsistent.

The regulatory code that determines whether and how a given genetic variant affects the function of a regulatory element remains poorly understood for most classes of regulatory variation. Indeed the large majority of bioinformatics tools have been developed to predict the pathogenicity of genetic variants in coding sequences or conserved splice sites. Computational algorithms for the prediction of non-coding deleterious variants associated with rare genetic diseases are faced with special challenges owing to the rarity of confirmed pathogenic mutations. Indeed in this context classical machine learning methods are biased toward neutral variants that constitute the large majority of genetic variation, and are not able to detect the potential deleterious variants that constitute only a tiny minority of all known genetic variation. We recently proposed hyperSMURF, hyper-ensemble of SMOTE Undersampled Random Forests, an ensemble approach explicitly designed to deal with the huge imbalance between deleterious and neutral variants, and able to significantly outperform state-of-the-art methods for the prediction of non-coding variants associated with Mendelian diseases. Despite its successful application to the detection of deleterious single nucleotide variants (SNV) as well as to small insertions or deletions (indels), hyperSMURF is a method that depends on several learning parameters, that strongly influence its overall performances. In this work we experimentally show that by tuning hyperSMURF parameters we can significantly boost the performance of the method, thus predicting with significantly better precision and recall rare SNVs associated with Mendelian diseases.

Megalencephaly-capillary malformation (MCAP) syndrome is an overgrowth syndrome that is diagnosed by clinical criteria. Recently, somatic and germline variants in genes that are involved in the PI3K-AKT pathway (AKT3, PIK3R2 and PIK3CA) have been described to be associated with MCAP and/or other related megalencephaly syndromes. We performed trio-exome sequencing in a 6-year-old boy and his healthy parents. Clinical features were macrocephaly, cutis marmorata, angiomata, asymmetric overgrowth, developmental delay, discrete midline facial nevus flammeus, toe syndactyly and postaxial polydactyly—thus, clearly an MCAP phenotype. Exome sequencing revealed a pathogenic de novo germline variant in the PTPN11 gene (c.1529A>G; p.(Gln510Arg)), which has so far been associated with Noonan, as well as LEOPARD syndrome. Whole-exome sequencing (>100 × coverage) did not reveal any alteration in the known megalencephaly genes. However, ultra-deep sequencing results from saliva (>1000 × coverage) revealed a 22% mosaic variant in PIK3CA (c.2740G>A; p.(Gly914Arg)). To our knowledge, this report is the first description of a PTPN11 germline variant in an MCAP patient. Data from experimental studies show a complex interaction of SHP2 (gene product of PTPN11) and the PI3K-AKT pathway. We hypothesize that certain PTPN11 germline variants might drive toward additional second-hit alterations.

Background The last two human genome assemblies have extended the previous linear golden-path paradigm of the human genome to a graph-like model to better represent regions with a high degree of structural variability. The new model offers opportunities to improve the technical validity of variant calling in whole-genome sequencing (WGS). Methods We developed an algorithm that analyzes the patterns of variant calls in the 178 structurally variable regions of the GRCh38 genome assembly, and infers whether a given sample is most likely to contain sequences from the primary assembly, an alternate locus, or their heterozygous combination at each of these 178 regions. We investigate 121 in-house WGS datasets that have been aligned to the GRCh37 and GRCh38 assemblies. Results We show that stretches of sequences that are largely but not entirely identical between the primary assembly and an alternate locus can result in multiple variant calls against regions of the primary assembly. In WGS analysis, this results in characteristic and recognizable patterns of variant calls at positions that we term alignable scaffold-discrepant positions (ASDPs). In 121 in-house genomes, on average 51.8±3.8 of the 178 regions were found to correspond best to an alternate locus rather than the primary assembly sequence, and filtering these genomes with our algorithm led to the identification of 7863 variant calls per genome that colocalized with ASDPs. Additionally, we found that 437 of 791 genome-wide association study hits located within one of the regions corresponded to ASDPs. Conclusions Our algorithm uses the information contained in the 178 structurally variable regions of the GRCh38 genome assembly to avoid spurious variant calls in cases where samples contain an alternate locus rather than the corresponding segment of the primary assembly. These results suggest the great potential of fully incorporating the resources of graph-like genome assemblies into variant calling, but also underscore the importance of developing computational resources that will allow a full reconstruction of the genotype in personal genomes. Our algorithm is freely available at https://github.com/charite/asdpex. Electronic supplementary material The online version of this article (doi:10.1186/s13073-016-0383-z) contains supplementary material, which is available to authorized users.

© 2016 American Society of Human Genetics The interpretation of non-coding variants still constitutes a major challenge in the application of whole-genome sequencing in Mendelian disease, especially for single-nucleotide and other small non-coding variants. Here we present Genomiser, an analysis framework that is able not only to score the relevance of variation in the non-coding genome, but also to associate regulatory variants to specific Mendelian diseases. Genomiser scores variants through either existing methods such as CADD or a bespoke machine learning method and combines these with allele frequency, regulatory sequences, chromosomal topological domains, and phenotypic relevance to discover variants associated to specific Mendelian disorders. Overall, Genomiser is able to identify causal regulatory variants as the top candidate in 77% of simulated whole genomes, allowing effective detection and discovery of regulatory variants in Mendelian disease.

Strømme syndrome was first described by Strømme et al. (1993) in siblings presenting with "apple peel" type intestinal atresia, ocular anomalies and microcephaly. The etiology remains unknown to date. We describe the long-term clinical follow-up data for the original pair of siblings as well as two previously unreported siblings with a severe phenotype overlapping that of the Strømme syndrome including fetal autopsy results. Using family-based whole-exome sequencing, we identified truncating mutations in the centrosome gene CENPF in the two nonconsanguineous Caucasian sibling pairs. Compound heterozygous inheritance was confirmed in both families. Recently, mutations in this gene were shown to cause a fetal lethal phenotype, the phenotype and functional data being compatible with a human ciliopathy [Waters et al., 2015]. We show for the first time that Strømme syndrome is an autosomal-recessive disease caused by mutations in CENPF that can result in a wide phenotypic spectrum.

International audience; Retinal dystrophies (RD) constitute a group of blinding diseases that are characterized by clinical variability and pronounced genetic heterogeneity. The different nonsyndromic and syndromic forms of RD can be attributed to mutations in more than 200 genes. Consequently, next generation sequencing (NGS) technologies are among the most promising approaches to identify mutations in RD. We screened a large cohort of patients comprising 89 independent cases and families with various subforms of RD applying different NGS platforms. While mutation screening in 50 cases was performed using a RD gene capture panel, 47 cases were analyzed using whole exome sequencing. One family was analyzed using whole genome sequencing. A detection rate of 61% was achieved including mutations in 34 known and two novel RD genes. A total of 69 distinct mutations were identified, including 39 novel mutations. Notably, genetic findings in several families were not consistent with the initial clinical diagnosis. Clinical reassessment resulted in refinement of the clinical diagnosis in some of these families and confirmed the broad clinical spectrum associated with mutations in RD genes.

Intellectual disability (ID) affects 2-3% of the population. In the past, many genetic causes of ID remained unidentified due to its vast heterogeneity. Recently, whole exome sequencing (WES) studies have shown that de novo variants underlie a significant portion of sporadic cases of ID. Applying WES to patients with ID or global developmental delay at different centers, we identified three individuals with distinct de novo variants in HIVEP2 (human immunodeficiency virus type I enhancer binding protein), which belongs to a family of zinc-finger-containing transcriptional proteins involved in growth and development. Two of the variants were nonsense changes, and one was a 1 bp deletion resulting in a premature stop codon that was reported previously without clinical detail. In silico prediction programs suggest loss-of-function in the mutated allele leading to haploinsufficiency as a putative mechanism in all three individuals. All three patients presented with moderate-to-severe ID, minimal structural brain anomalies, hypotonia, and mild dysmorphic features. Growth parameters were in the normal range except for borderline microcephaly at birth in one patient. Two of the patients exhibited behavioral anomalies including hyperactivity and aggression. Published functional data suggest a neurodevelopmental role for HIVEP2, and several of the genes regulated by HIVEP2 are implicated in brain development, for example, SSTR-2, c-Myc, and genes of the NF-κB pathway. In addition, HIVEP2-knockout mice exhibit several working memory deficits, increased anxiety, and hyperactivity. On the basis of the genotype-phenotype correlation and existing functional data, we propose HIVEP2 as a causative ID gene.

Background Splenic marginal zone lymphoma (SMZL) is an indolent B-cell non-Hodgkin lymphoma and represents the most common primary malignancy of the spleen. Its precise molecular pathogenesis is still unknown and specific molecular markers for diagnosis or possible targets for causal therapies are lacking. Methods We performed whole exome sequencing (WES) and copy number analysis from laser-microdissected tumor cells of two primary SMZL discovery cases. Selected somatic single nucleotide variants (SNVs) were analyzed using pyrosequencing and Sanger sequencing in an independent validation cohort. Results Overall, 25 nonsynonymous somatic SNVs were identified, including known mutations in the NOTCH2 and MYD88 genes. Twenty-three of the mutations have not been associated with SMZL before. Many of these seem to be subclonal. Screening of 24 additional SMZL for mutations at the same positions found mutated in the WES approach revealed no recurrence of mutations for ZNF608 and PDE10A, whereas the MYD88 L265P missense mutation was identified in 15 % of cases. An analysis of the NOTCH2 PEST domain and the whole coding region of the transcription factor SMYD1 in eight cases identified no additional case with a NOTCH2 mutation, but two additional cases with SMYD1 alterations. Conclusions In this first WES approach from microdissected SMZL tissue we confirmed known mutations and discovered new somatic variants. Recurrence of MYD88 mutations in SMZL was validated, but NOTCH2 PEST domain mutations were relatively rare (10 % of cases). Recurrent mutations in the transcription factor SMYD1 have not been described in SMZL before and warrant further investigation. Electronic supplementary material The online version of this article (doi:10.1186/s12885-015-1766-z) contains supplementary material, which is available to authorized users.

SATB2 is an evolutionarily highly conserved chromatin remodeling gene located on chromosome 2q33.1. Vertebrate animal models have shown that Satb2 has a crucial role in craniofacial patterning and osteoblast differentiation, as well as in determining the fates of neuronal projections in the developing neocortex. In humans, chromosomal translocations and deletions of 2q33.1 leading to SATB2 haploinsufficiency are associated with cleft palate (CP), facial dysmorphism and intellectual disability (ID). A single patient carrying a nonsense mutation in SATB2 has been described to date. In this study, we performed trio-exome sequencing in a 3-year-old girl with CP and severely delayed speech development, and her unaffected parents. Previously, the girl had undergone conventional and molecular karyotyping (microarray analysis), as well as targeted analysis for different diseases associated with developmental delay, including Angelman syndrome, Rett syndrome and Fragile X syndrome. No diagnosis could be established. Exome sequencing revealed a de novo nonsense mutation in the SATB2 gene (c.715C>T; p.R239*). The identification of a second patient carrying a de novo nonsense mutation in SATB2 confirms that this gene is essential for normal craniofacial patterning and cognitive development. Based on our data and the literature published so far, we propose a new clinically recognizable syndrome – the SATB2-associated syndrome (SAS). SAS is likely to be underdiagnosed and should be considered in children with ID, severe speech delay, cleft or high-arched palate and abnormal dentition with crowded and irregularly shaped teeth.

This is an Open Access article distributed under the terms of the Creative Commons Attribution License.-- et al., [Background]: There is tremendous potential for genome sequencing to improve clinical diagnosis and care once it becomes routinely accessible, but this will require formalizing research methods into clinical best practices in the areas of sequence data generation, analysis, interpretation and reporting. The CLARITY Challenge was designed to spur convergence in methods for diagnosing genetic disease starting from clinical case history and genome sequencing data. DNA samples were obtained from three families with heritable genetic disorders and genomic sequence data were donated by sequencing platform vendors. The challenge was to analyze and interpret these data with the goals of identifying disease-causing variants and reporting the findings in a clinically useful format. Participating contestant groups were solicited broadly, and an independent panel of judges evaluated their performance. [Results]: A total of 30 international groups were engaged. The entries reveal a general convergence of practices on most elements of the analysis and interpretation process. However, even given this commonality of approach, only two groups identified the consensus candidate variants in all disease cases, demonstrating a need for consistent fine-tuning of the generally accepted methods. There was greater diversity of the final clinical report content and in the patient consenting process, demonstrating that these areas require additional exploration and standardization. [Conclusions]: The CLARITY Challenge provides a comprehensive assessment of current practices for using genome sequencing to diagnose and report genetic diseases. There is remarkable convergence in bioinformatic techniques, but medical interpretation and reporting are areas that require further development by many groups., This work was supported by funds provided through the Gene Partnership and the Manton Center for Orphan Disease Research at Boston Children’s Hospital and the Center for Biomedical Informatics at Harvard Medical School and by generous donations in-kind of genomic sequencing services by Life Technologies (Carlsbad, CA, USA) and Complete Genomics (Mountain View, CA, USA).

Background Metagenomics is the study of environmental samples using sequencing. Rapid advances in sequencing technology are fueling a vast increase in the number and scope of metagenomics projects. Most metagenome sequencing projects so far have been based on Sanger or Roche-454 sequencing, as only these technologies provide long enough reads, while Illumina sequencing has not been considered suitable for metagenomic studies due to a short read length of only 35 bp. However, now that reads of length 75 bp can be sequenced in pairs, Illumina sequencing has become a viable option for metagenome studies. Results This paper addresses the problem of taxonomical analysis of paired reads. We describe a new feature of our metagenome analysis software MEGAN that allows one to process sequencing reads in pairs and makes assignments of such reads based on the combined bit scores of their matches to reference sequences. Using this new software in a simulation study, we investigate the use of Illumina paired-sequencing in taxonomical analysis and compare the performance of single reads, short clones and long clones. In addition, we also compare against simulated Roche-454 sequencing runs. Conclusion This work shows that paired reads perform better than single reads, as expected, but also, perhaps slightly less obviously, that long clones allow more specific assignments than short ones. A new version of the program MEGAN that explicitly takes paired reads into account is available from our website.